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#SEE PACKAGE MAPVIEW #SLIDE 8 #opts_chunk : echo, eval TRUE, message, warning FALSE. fig.retina = 3
#maybe can use community detection to find the relationship
loyalty_data <- read_csv("MC2/loyalty_data.csv")##
## -- Column specification --------------------------------------------------------------------------------------------------------------------------------------------
## cols(
## timestamp = col_character(),
## location = col_character(),
## price = col_double(),
## loyaltynum = col_character()
## )loyalty_data$timestamp <- date_time_parse(loyalty_data$timestamp,
zone = "",
format = "%m/%d/%Y")
cc_data <- read_csv("MC2/cc_data.csv")##
## -- Column specification --------------------------------------------------------------------------------------------------------------------------------------------
## cols(
## timestamp = col_character(),
## location = col_character(),
## price = col_double(),
## last4ccnum = col_double(),
## id = col_double()
## )cc_data$timestamp_date <- date_time_parse(cc_data$timestamp,
zone = "",
format = "%m/%d/%Y")
cc_data$timestamp <- date_time_parse(cc_data$timestamp,
zone = "",
format = "%m/%d/%Y %H:%M")
cc_data## # A tibble: 1,490 x 6
## timestamp location price last4ccnum id timestamp_date
## <dttm> <chr> <dbl> <dbl> <dbl> <dttm>
## 1 2014-01-06 07:28:00 Brew've Been Served 11.3 4795 34 2014-01-06 00:00:00
## 2 2014-01-06 07:34:00 Hallowed Grounds 52.2 7108 12 2014-01-06 00:00:00
## 3 2014-01-06 07:35:00 Brew've Been Served 8.33 6816 20 2014-01-06 00:00:00
## 4 2014-01-06 07:36:00 Hallowed Grounds 16.7 9617 18 2014-01-06 00:00:00
## 5 2014-01-06 07:37:00 Brew've Been Served 4.24 7384 17 2014-01-06 00:00:00
## 6 2014-01-06 07:38:00 Brew've Been Served 4.17 5368 NA 2014-01-06 00:00:00
## 7 2014-01-06 07:42:00 Coffee Cameleon 28.7 7253 6 2014-01-06 00:00:00
## 8 2014-01-06 07:43:00 Brew've Been Served 9.6 4948 NA 2014-01-06 00:00:00
## 9 2014-01-06 07:43:00 Brew've Been Served 16.9 9683 33 2014-01-06 00:00:00
## 10 2014-01-06 07:47:00 Hallowed Grounds 16.5 8129 NA 2014-01-06 00:00:00
## # ... with 1,480 more rowsgps <- read_csv("MC2/gps.csv")##
## -- Column specification --------------------------------------------------------------------------------------------------------------------------------------------
## cols(
## Timestamp = col_character(),
## id = col_double(),
## lat = col_double(),
## long = col_double()
## )gps$Date <-date_time_parse(gps$Timestamp,
zone = "",
format = "%m/%d/%Y")
gps$Timestamp <-date_time_parse(gps$Timestamp,
zone = "",
format = "%m/%d/%Y %H:%M:%S")
gps_sort <- gps[order(gps$Timestamp, gps$id),]stop_fin = data.frame()
for (id_1 in unique(gps_sort$id))
{
make_stop = data.frame()
gps_id <- gps_sort %>%
filter(id==id_1)
for(i in 1:nrow(gps_id))
{
if(i < nrow(gps_id))
{
start <- gps_id$Timestamp[i]
end <- gps_id$Timestamp[i+1]
} else
{
start <- gps_id$Timestamp[i]
end <- gps_id$Timestamp[i]
}
elapsed.time <- start%--%end
time_duration <- as.duration(elapsed.time)
if(time_duration > 120)
{
make_stop = rbind(make_stop,cbind(gps_id[i,],time_duration,i),cbind(gps_id[i+1,],time_duration,i))
}
}
stop_fin = rbind(stop_fin, make_stop)
}Abila_st <- st_read(dsn = "MC2/Geospatial",
layer = "Abila")## Reading layer `Abila' from data source
## `C:\Users\jovinkahartanto\Desktop\SMU MITB-JOVINKA\May 2021 - Aug 2021\Visual Analytics\Assignment\Assignment-ISSS608\MC2\Geospatial' using driver `ESRI Shapefile'
## Simple feature collection with 3290 features and 9 fields
## Geometry type: LINESTRING
## Dimension: XY
## Bounding box: xmin: 24.82401 ymin: 36.04502 xmax: 24.90997 ymax: 36.09492
## Geodetic CRS: WGS 84P <- npts(Abila_st, by_feature = TRUE)
abila_st_2 <- cbind(Abila_st,P) %>% filter(P > 1)
gps_sf <- st_as_sf(stop_fin,
coords = c("long","lat"),
crs=4326)
gps_path <- gps_sf %>%
group_by(id, i) %>%
summarize(m_Timestamp = mean(Timestamp),
do_union = FALSE ) %>%
st_cast("LINESTRING")## `summarise()` has grouped output by 'id'. You can override using the `.groups` argument.gps_path_selected <- gps_path %>%
filter(id==5)
bgmap <- raster("MC2/Geospatial/MC2-tourist.TIF")
tmap_mode("view")## tmap mode set to interactive viewingtm_shape(bgmap) +
tm_rgb(bgmap, r = 1, g = 2, b = 3,
alpha = NA,
saturation = 1,
interpolate = TRUE,
max.value = 255) +
tm_shape(abila_st_2) +
tm_lines(col = "red", scale = 1)+
tm_shape(gps_path_selected[c(2,8,13,18,20),]) +
tm_lines(col = "black", scale =7, interactive = TRUE) +
tm_text("i", size = 4, remove.overlap = TRUE, overwrite.lines = TRUE, just = "top")## stars object downsampled to 1303 by 768 cells. See tm_shape manual (argument raster.downsample)## Warning: col specification in tm_raster is ignored, since stars object contains a 3rd dimension, where its values are used to create facetscc_selected <- cc_data %>% filter(location == "Abila Zacharo")
cc_selected$timestamp <- as_date_time(cc_selected$timestamp)
sf_selected = stop_fin %>% filter(id == 35, i == 682)
plot_id = data.frame()
for(id1 in unique(stop_fin$id))
{
potential_id = data.frame()
sf_selected_1 = stop_fin %>% filter(id == id1)
for (i1 in unique(sf_selected_1$i))
{
sf_selected_2 = stop_fin %>% filter(id == id1, i == i1)
if(cc_selected$timestamp[72] >= sf_selected_2[1,1] & cc_selected$timestamp[72] <= sf_selected_2[2,1])
{
potential_id = rbind(potential_id, sf_selected_2[1:2,])
}
}
plot_id = rbind(plot_id,potential_id)
}
gps_sf <- st_as_sf(plot_id,
coords = c("long","lat"),
crs=4326)
gps_path <- gps_sf %>%
group_by(id, i) %>%
summarize(m_Timestamp = mean(Timestamp),
do_union = FALSE ) %>%
st_cast("LINESTRING")## `summarise()` has grouped output by 'id'. You can override using the `.groups` argument.gps_path_selected <- gps_path
bgmap <- raster("MC2/Geospatial/MC2-tourist.TIF")
tmap_mode("view")## tmap mode set to interactive viewingtm_shape(bgmap) +
tm_rgb(bgmap, r = 1, g = 2, b = 3,
alpha = NA,
saturation = 1,
interpolate = TRUE,
max.value = 255) +
tm_shape(abila_st_2) +
tm_lines(col = "red", scale = 1)+
tm_shape(gps_path_selected) +
tm_lines(col = "black", scale =7, interactive = TRUE) +
tm_text("id", size = 4, remove.overlap = TRUE, overwrite.lines = TRUE, just = "top")## stars object downsampled to 1303 by 768 cells. See tm_shape manual (argument raster.downsample)## Warning: col specification in tm_raster is ignored, since stars object contains a 3rd dimension, where its values are used to create facetscar_assignments <- read_csv("MC2/car-assignments.csv")##
## -- Column specification --------------------------------------------------------------------------------------------------------------------------------------------
## cols(
## LastName = col_character(),
## FirstName = col_character(),
## CarID = col_double(),
## CurrentEmploymentType = col_character(),
## CurrentEmploymentTitle = col_character()
## )car_assignments## # A tibble: 44 x 5
## LastName FirstName CarID CurrentEmploymentType CurrentEmploymentTitle
## <chr> <chr> <dbl> <chr> <chr>
## 1 Calixto Nils 1 Information Technology IT Helpdesk
## 2 Azada Lars 2 Engineering Engineer
## 3 Balas Felix 3 Engineering Engineer
## 4 Barranco Ingrid 4 Executive SVP/CFO
## 5 Baza Isak 5 Information Technology IT Technician
## 6 Bergen Linnea 6 Information Technology IT Group Manager
## 7 Orilla Elsa 7 Engineering Drill Technician
## 8 Alcazar Lucas 8 Information Technology IT Technician
## 9 Cazar Gustav 9 Engineering Drill Technician
## 10 Campo-Corrente Ada 10 Executive SVP/CIO
## # ... with 34 more rows# gps_path_daily <- gps_sf %>%
# group_by(id,Timestamp_Date) %>%
# summarize(m = mean(Timestamp_Date),
# do_union = FALSE ) %>%
# st_cast("LINESTRING")# gps_path_daily_selected <- gps_path_daily %>%
# filter(id==1 & Timestamp_Date == "2014-01-06")
#
# bgmap <- raster("MC2/Geospatial/MC2-tourist.TIF")
#
# tmap_mode("view")
# tm_shape(bgmap) +
# tm_rgb(bgmap, r = 1, g = 2, b = 3,
# alpha = NA,
# saturation = 1,
# interpolate = TRUE,
# max.value = 255) +
# tm_shape(gps_path_daily_selected) +
# tm_lines()# gps1 <- gps_sf %>%
# filter(id == 1 & Timestamp > "2014-01-06 07:57:01" & Timestamp < "2014-01-06 07:57:48")
#
# gps_path1 <- gps1 %>%
# group_by(id) %>%
# summarize(m = mean(Timestamp),
# do_union = FALSE ) %>%
# st_cast("LINESTRING")
#
#
# tmap_mode("view")
# tm_shape(bgmap) +
# tm_rgb(bgmap, r = 1, g = 2, b = 3,
# alpha = NA,
# saturation = 1,
# interpolate = TRUE,
# max.value = 255) +
# tm_shape(gps_path1) +
# tm_lines()